A near real-time forecasting system for shallow landslides at regional scale based on distributed modelling and weather predictions

This work presents the development of a forecasting system for shallow rainfall-induced landslides, designed to provide daily near–real-time outputs at a regional scale. The system integrates hydrological and slope stability distributed modelling with daily rainfall forecasts from a weather prediction model to generate daily shallow landslide initiation forecasts for an area of hundreds of square kilometers. Research activities focused on: i) the optimization of the HIRESSS model to enhance computational efficiency and physical consistency for real-time forecasting; (ii) the calibration of the system based on the hydrological, geotechnical, morphological and climatic characteristics of the study area; and (iii) developing algorithms enabling the continuous use of meteorological forecasts and the production of both distributed (grid-based) and aggregated (sub-basin) outputs. The operational system based on the HIRESSS model has been active since June 2024 for the Alert Zone B of the Aosta Valley Region. It uses precipitation forecasts from the ICON-CH1 weather prediction model as dynamic input. The system generates landslide initiation susceptibility maps in terms of failure probability, with a 10 m spatial and 3-hour temporal resolution, producing forecastsfor the current and following day. Additionally, it provides aggregated probabilities at sub-basin level through a calibrated threshold system, supporting early warning activities. Recent developments have focused on optimizing the real-time dissemination of model outputs through an open-data platform ensuring transparency, accessibility, and operational usability for regional authorities and decision-makers. The developed system represents a significant step toward a fully operational landslide early warning system at the regional scale based on physically based distributed modelling and meteorological forecasting.